Distributed control systems (DCSs) are generally configured in modern process industries, such as power, petrochemicals, etc. A typical industrial process unit includes nearly 100 control loops to implement automatic control on main variables in production processes. Thus, control loops are the key assets to determine safe and efficient operations of industrial processes. It is very necessary to automatically evaluate the performance of industrial control loops, detect the control loops with poor performance promptly and provide the improvement potentials and technical solutions for the control performance, so that satisfactory control performance is achieved in the industrial processes over a long-term operation.
The basic principle of performance evaluation of a control loop is to find out an ideal value of a certain control performance index that can be achieved under some practical constraints, and to compare the ideal value as a reference value with the current value of the performance index to evaluate the control performance of the control loop. The most widely accepted and commonly used control loop performance evaluation method is the one based on minimum variance control, proposed by Professor T. Harris from Queen's University in Canada in 1989. However, the method has two major limitations:
First, it does not consider the restriction from the structure of a proportional-integral-derivative (PID) controller commonly used in the industry on the dynamic control performance, so that a PID controller based control loop with better control performance is misjudged as having poor performance;
Second, a variance is used as a performance index, which is more suitable for evaluating the performance of a control loop in overcoming random noise, but not suitable for evaluating the performance of tracking a reference value and overcoming measurable (deterministic) external disturbances.
Therefore, since 2004, Professor T. F. Edgar from U.S. Texas University and other experts have studied the effect of the structure of the PID controller on the control performance. An approximate value of an ideal value of a control performance index of the PID controller can be obtained by adopting performance indices such as an integrated absolute value of a control error through numerical approximation method. However, in industrial applications, the above available technologies have a major problem:
Besides PID controllers, there are also many other control modules, such as filters, piece-wise linear functions and dead-bands which are indispensable to the industrial control loop. Thus, when the performance of the control loop is evaluated, it is necessary to consider the important influence of these control modules on the control performance. However, in the available technologies, only one control module, named the PID controller, instead of many other control modules objectively existing in the industrial control loop, is considered, and important influence of the other control modules on the control performance cannot be evaluated, resulting in inaccurate, incomplete or even erroneous performance evaluation results.
In summary, there are no effective solutions in the available technologies, and the performance of the control loop in process industries cannot be accurately and comprehensively evaluated.